One-dimensional Sequence Signal Detection Method for Marine Target Based on Deep Learning

 In this paper, the feature generalization learning ability of deep learning is used to process the signal time series amplitude information. From the perspectives of signal prediction and feature classification, respectively, long short-term memory networks (LSTM) and convolutional neural networks (CNN) are used for the detection of target’s one-dimensional sequence radar signal. The target detection method based on LSTM sequence prediction uses the sea clutter signal amplitude time series to train the network, and then uses the trained network to predict subsequent sequences, and compares it with subsequently measured signals to achieve target detection. In the target detection method based on CNN sequence classification, the intercepted sea clutter signal and the target signal amplitude sequence are used as dataset samples to train the one-dimensional convolution kernel CNN so that it can identify the target clutter signal feature, thereby achieving the target detection. Finally, the two methods were verified using IPIX and CSIR measured sea clutter data. The results show that both methods can detect sea-surface targets in one-dimensional sequence signals, but the real-time performance of LSTM prediction methods for long sequence detection needs to be further improved. CNN classification method can realize real-time detection, but using only signal amplitude information, the detection performance still needs to be further improved.